Local-First · Zero Data Exposure · Enterprise Ready

Your AI assistant.
Your data.
Their servers.

Jarvis connects to Jira, Confluence, GitHub, Slack and your Mac — executing real work from a single prompt. All on your machine. Nothing leaves.

Download for macOS See why local wins →

Free & open source. macOS 13+ · Apple Silicon & Intel · No cloud required

Jarvis — Cowork Mode · gemma4:26b · Local
Analyze board 1540. Give me a sprint bleed report — velocity trends, hidden blockers, silent_suffering tickets, and 3 executive actions. Email it to me when done.
Fetching sprint data and cross-referencing GitHub activity…
⟳ analyze_active_sprint · board 1540 ✓ get_sprint_issues · 24 issues ✓ send_apple_mail · sanjay.v@elastic.co
Sprint Bleed Report — Sprint 47

📊 Velocity: 34 pts completed / 62 pts planned (55% — critical)
🔴 7 silent suffering tickets — avg 14h logged on ≤3pt stories
🔒 3 hidden blockers found in ITDEV-2341, 2389, 2401
📬 Full report sent to sanjay.v@elastic.co
0 bytes sent to external AI APIs
30+ enterprise tool integrations
100% offline capable
SOC2 / HIPAA friendly by design
Open source. Auditable. Forever free.
Connects to your entire enterprise stack
Jira
Confluence
Slack
GitHub
Apple Mail
macOS Filesystem
Ollama (local)
Gemini (cloud fallback)
The Local Advantage

Your data is your
competitive edge.

Every time you paste a sprint report into ChatGPT, that data trains someone else's model. Jarvis runs entirely on your hardware — the AI comes to your data, not the other way around.

🔐
Zero Data Exposure
Sprint data, Jira tickets, source code, Slack messages — all processed on your machine. No API calls to OpenAI, Anthropic, or any third-party LLM service. Your IP stays yours.
0 bytes
sent to external AI servers per session
⚡
No Latency Tax
Local inference on gemma4:26b via Ollama has no round-trip to a cloud API. No rate limits, no throttling, no 503s. First token in milliseconds, not seconds. Works offline on a plane.
100%
uptime regardless of internet connectivity
💰
No Per-Token Bills
A team of 20 engineers using GPT-4o for daily Jira and code analysis can cost $3,000–$8,000/month. Jarvis on local hardware: $0/month in AI API costs, forever. The GPU pays for itself in weeks.
$0/mo
AI API costs — ever

Jarvis Local vs. Cloud AI Tools

Side-by-side reality check.

Capability
✦ Jarvis (Local)
ChatGPT / Copilot
Data PrivacyYour prompts & results
✓ Never leaves device
✗ Sent to vendor servers
Compliance (SOC2, HIPAA, GDPR)Regulated industries
✓ Compliant by design
⚠ Requires DPA & review
Live Jira / GitHub / Slack AccessReal-time tool execution
✓ Native, always-on
⚠ Plugin-only, unreliable
Monthly AI CostFor a 20-person team
✓ $0 / month
✗ $2,000–$8,000 / month
Offline OperationNo internet needed
✓ Fully offline
✗ Requires internet
Model Training on Your DataDoes vendor learn from you?
✓ Never
✗ Often by default
Custom Skill PersonasRole-specific AI behavior
✓ Unlimited custom skills
⚠ Limited / expensive
Voice Mode (offline)No Google/Apple servers
✓ faster-whisper, local
✗ Requires cloud STT
Technical Architecture

Three layers.
One intelligent system.

Electron shell + Python tool sidecar + pluggable AI backend. Every layer is auditable, replaceable, and runs entirely on your hardware.

① UI Layer — React · Electron Renderer
Cowork ModeChat + Skill Personas
Jarvis Code IDEMonaco + AI Assistant
Schedule ModeJira Task Scheduler
Voice ModeOffline STT/TTS
Analytics PanelToken + Activity
IPC (contextBridge)
② Process Bridge — Electron Main · Node.js
Agentic Tool Loop12 iterations · dedup cache
IPC Handlerschat:send, file:pick…
Voice Workerfaster-whisper persistent
Screen Capturescreencapture -x
File HandlerPDF · CSV · Images · Code
electron-storeSettings · Keychain
HTTP · localhost only
③ Intelligence Layer — AI Backends + Python Tool Sidecar (Flask :7821)
Ollama · localgemma4:26b · qwen3-coder
Gemini · cloudAuto-fallback when offline
jira_agent_clone.pyJira · Confluence
scrum_analytics.pySprint intelligence
github.pyIssues · PRs · Releases
slack.pyMessages · Search
mac_controller.pyMail · AppleScript
voice_worker.pyPersistent Whisper
🔑
The Key Insight: The AI comes to your tools, not the other way around
Every tool call happens over localhost:7821 — never routed through an external service. Credentials are injected per-call from macOS Keychain and never logged. The Python sidecar auto-restarts if it crashes and kills stale port squatters on boot.
The Agentic Tool Loop

Executes. Doesn't just suggest.

When you send a message, Jarvis enters a 12-iteration execution loop. Each iteration calls the LLM with accumulated tool results, executes new tool requests, caches results to avoid redundant API calls, and injects a synthesis nudge when the model tries to re-fetch already-retrieved data.

  • Adaptive context: 32K → 65K tokens based on conversation depth
  • Deduplication cache: same tool + same args = reuse result instantly
  • Gemini cloud fallback when Ollama is unreachable — zero config
  • Per-credential Keychain injection — never in environment variables
Tool Loop · Iteration 2/12
// iter=2 model=gemma4:26b ctx=49152
CALLING analyze_active_sprint {board_id: 1540}
✓ RESULT 24 issues · 34/62 pts · 7 alerts
CALLING send_apple_mail {recipient: "sanjay.v@…"}
✓ RESULT Email sent successfully
// No tool_calls in response → returning final synthesis
→ FINAL Sprint bleed report generated & emailed
Business Value

Real ROI.
Measurable impact.

Jarvis isn't a demo. It's the difference between a scrum master spending 3 hours on sprint reports and 15 minutes. Across a team. Every week.

📊
3h→15m
sprint report time
Sprint Intelligence
Jarvis fetches live sprint data, identifies velocity drops, hidden blockers, silent-suffering tickets (high hours / low points), and chronic spillovers — then generates an executive report and emails it in one prompt. What took a scrum master hours now takes seconds.
Scrum Masters Engineering Managers VPs of Engineering
🔐
$0
compliance exposure
Compliance by Architecture
For teams in healthcare, finance, or government — the question isn't "is this compliant?" it's "can you prove no data left your perimeter?" With Jarvis, the answer is trivially yes. All processing happens on your hardware. No DPA required. No vendor risk review.
HIPAA SOC2 GDPR ISO 27001
👨‍💻
10×
developer leverage
Developer Productivity
Jarvis Code puts a Monaco IDE with a qwen3-coder:30b AI assistant directly in the desktop. Ask for a security review, click Apply — changes land in the editor. No copy-paste, no context switching, no token bills for every keystroke. Local. Always on.
Code Review Refactoring Security Analysis
🤖
30+
automated workflows
Cross-Platform Automation
A single prompt can search Jira, cross-reference GitHub PRs, generate a Confluence page, and post a Slack notification — all in one shot. No Zapier. No n8n. No glue code. The LLM orchestrates the entire workflow using live tool calls.
Product Managers DevOps Security Teams
Real-World Use Cases

What teams actually do
with Jarvis every day.

These aren't demos. They're Monday morning workflows.

Scrum Master
"Run the sprint bleed report for board 1540 and email it to me."
Fetches sprint data, identifies velocity gaps, blockers, and overloaded devs, formats an executive report, sends via Apple Mail.
analyze_sprintsend_email
CISO / Security
"Find all open security issues in GitHub, cross-ref with Jira, create a risk matrix in Confluence."
Searches GitHub issues, queries Jira for matching tickets, identifies coverage gaps, publishes a risk matrix Confluence page.
search_githubsearch_jiracreate_confluence
Product Manager
"Find Jira backlog items older than 60 days with no comments. Triage: kill, defer, or prioritize. Post to #pm-triage."
Queries Jira, applies categorization logic, formats a triage table, posts directly to Slack channel.
search_jirasend_slack
Engineering Lead
"Get the last 3 GitHub releases, map PRs to Jira tickets, generate release notes and create a Confluence page."
Fetches releases, maps PR titles to Jira keys, generates customer-facing release notes, publishes to Confluence in your template.
github_releasessearch_jiracreate_confluence
Developer
Open file in Jarvis Code → "Security review this file and apply the fixes."
AI reads the file, identifies XSS vectors and unsafe patterns, generates fixed code, you click ⚡ Apply — changes land in the editor instantly.
read_fileapply_to_editor
Team Lead
"Pull what I worked on in Jira yesterday and post my standup to #daily-standup."
Queries Jira for your recent activity, writes a clean standup in your team's format, posts to the correct Slack channel.
search_jirasend_slack
Offline Voice Mode

Hands-free AI that works on a plane.

Jarvis uses faster-whisper (Whisper base model) running entirely locally for speech-to-text — no Google, no Apple servers. The model loads once into a persistent Python worker and stays resident, so each listen cycle is near-instant. Responses are spoken aloud via macOS Say.

  • No internet required — works completely offline
  • ~5s one-time model load on first activation, then instant
  • Auto-reconnect loop — silence returns to listening
  • Voice commands trigger full agentic workflows
🎙️
Listening…
faster-whisper · base model · CPU
No internet · No Google STT
Active Skill Personas
🏃
Scrum Master
Sprint analysis · Velocity · Blockers
ACTIVE
🔐
CISO
Security · Risk · Compliance
📊
Data Analyst
Metrics · Tables · Summaries
✨
Custom Skill
Define your own persona
Skill Personas

One AI. Every role on your team.

Switch between purpose-built skill personas that reshape how Jarvis thinks, writes, and prioritizes. Each skill injects a specialized system prompt — Scrum Master mode focuses on sprint health, CISO mode emphasizes risk and compliance, Data Analyst mode demands numbers and tables.

  • Skills reset to Default on every app restart — no accidental context bleed
  • Custom skills persist and are editable anytime
  • Combined with any model: gemma4, qwen3-coder, gemini
  • Feedback loop: 👍/👎 ratings improve future responses
Installation

Up and running
in 5 minutes.

Requires macOS 13+ and Python 3.10+. Ollama is optional but recommended for full local operation.

Download Jarvis — Apple Silicon
macOS 13+ · Apple Silicon · Free & open source
1

Install Jarvis

Open the .dmg, drag Jarvis to Applications. On first launch macOS may show a security warning — right-click the app and choose Open.

2

Install Ollama + pull models

Download from ollama.com, then pull the recommended models:

ollama pull gemma4:26b

ollama pull qwen3-coder:30b

Low on RAM? gemma4:12b works great on 16GB machines.

3

Install Python dependencies

pip3 install flask faster-whisper SpeechRecognition pyaudio pdfminer.six

Or let Jarvis do it — first launch prompts to auto-install all requirements.

4

Configure credentials

Open Settings (⚙️ in sidebar). Enter your Jira URL + API token, GitHub token, Slack bot token, and optional Gemini key. Stored securely in macOS Keychain.

5

First prompt

"Analyze my active sprint on board [ID] and give me a health report"
Common issues
"fetch failed" on tool callRestart Jarvis — sidecar kills stale port 7821 on launch
Voice stuck at Listening…First activation loads Whisper model (~5s). Check mic in System Settings → Privacy → Microphone
Model not respondingRun ollama serve or enable Cloud mode with a Gemini key
"Jarvis is damaged" on launchRun in Terminal: xattr -cr /Applications/Jarvis.app — then relaunch. One-time only.
"Cannot be opened" Gatekeeper blockRight-click Jarvis.app → Open → Open Anyway. Apple requires paid signing for unsigned apps.